Research Article
BibTex RIS Cite

Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model

Year 2022, Volume: 7 Issue: 2, 179 - 194, 29.12.2022

Vehbi Emrah Atasoy

https://doi.org/10.58559/ijes.1207971

Abstract

In this study, Gaussian Process Regression (GPR) is utilised to accurately estimate fuel consumption. For this purpose, ten randomly determined flights performed by Boeing B737-800 twin-engine medium-haul narrow-bodied commercial aircraft are selected. In this context, actual flight data obtained from the Flight Data Recorder (FDR) is used to estimate fuel consumption during the climb-out phase. Different statistical tests, namely Root Mean Square Error (RMSE), coefficient of determination (R2), and Mean Absolute Error (MAE), are applied to evaluate the performance of the GPR in this paper. RMSE, R2, and MAE values for GPR is calculated to be 209.41, 0.99, and 111.38, respectively. As can be seen from the results of all statistical tests, the GPR model indicates successful performance.

Keywords

Fuel Consumption Gaussian Process Regression Climb-out phase

References

  • [1] Stolzer AJ. Fuel consumption modeling of a transport category aircraft using flight operations quality assurance data: a literature review. Journal of Air Transportation 2002; 7(1): 93.
  • [2] Lee DS, Pitari G, Grewe V, Gierens K, Penner JE, Petzold A, Prather MJ, Schumann U, Bais A, Berntsen T, Iachetti D, Lim LL, Sausen R. Transport impacts on atmosphere and climate: aviation, Atmospheric Environment 2010; 44(37): 4678-4734.
  • [3] Larsson J, Elofsson A, Sterner T, Åkerman J. International and national climate policies for aviation: a review. Climate Policy 2019; 19(6): 787-799.
  • [4] Brzozowski K. Kotlarz W. Modelling of air pollution on a military airfield. Atmospheric Environment 2005; 39(33): 6130-6139.
  • [5] Senzig DA, Fleming GG, Iovinelli RJ. Modeling of terminal-area airplane fuel consumption. Journal of Aircraft 2009; 46(4): 1089-1093.
  • [6] Collins B. Estimation of aircraft fuel consumption, Journal of Aircraft 1982; 19(11): 969–975.
  • [7] Patterson J, Noel GJ, Senzig DA, Roof CJ, Fleming GG. Analysis of departure and arrival profiles using real-time aircraft data. Journal of Aircraft 2009; 46(4): 1094-1103.
  • [8] Kim HJ, Baik H. Empirical method for estimating aircraft fuel consumption in ground operations. Transportation Research Record 2020; 2674(12): 385-394.
  • [9] Turgut ET, Rosen MA. Relationship between fuel consumption and altitude for commercial aircraft during descent: preliminary assessment with a genetic algorithm. Aerospace Science and Technology 2012; 17(1): 65-73.
  • [10] Zhu Q, Pei J, Liu X, Zhou Z. Analyzing commercial aircraft fuel consumption during descent: A case study using an improved K-means clustering algorithm. Journal of Cleaner Production 2019; 223: 869-882.
  • [11] Oruc R., Baklacioglu T. Modelling of fuel flow-rate of commercial aircraft for the climbing flight using cuckoo search algorithm. Aircraft Engineering and Aerospace Technology 2020; 92(3): 495-501
  • [12] Seymour K, Held M, Georges G, Boulouchos K. Fuel Estimation in Air Transportation: Modeling global fuel consumption for commercial aviation. Transportation Research Part D: Transport and Environment 2020; 88: 102528.
  • [13] Luo W, Wu Z, Chen C. An Aircraft Fuel Flow Model of Cruise Phase Based on LSTM and QAR Data. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 118-121). IEEE. 2020.
  • [14] Baumann S, Klingauf U. Modeling of aircraft fuel consumption using machine learning algorithms. CEAS Aeronautical Journal 2020; 11(1): 277-287.
  • [15] Baklacioglu T. Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks. Aerospace Science and Technology 2016; 49:52-62
  • [16] Elbir T. Estimation of engine emissions from commercial aircraft at a midsized Turkish airport. Journal of Environmental Engineering 2008; 134(3): 210-215.
  • [17] Rice CC. Validation of approach and climb-out times-in-mode for aircraft emissions computation. Transportation research record 2003; 1850(1): 79-82.
  • [18] Chati YS, Balakrishnan H. Data-driven modeling of aircraft engine fuel burn in climb out and approach. Transportation Research Record 2018; 2672(29): 1-11.
  • [19] Liati A, Schreiber D, Alpert PA, Liao Y, Brem BT, Arroyo PC, Eggenschwiler, PD. Aircraft soot from conventional fuels and biofuels during ground idle and climb-out conditions: Electron microscopy and X-ray micro- spectroscopy. Environmental Pollution 2019; 247: 658-667.
  • [20] Chilongola FD, Ahyudanari E. Aviation and aircraft engine emissions at Juanda International Airport. Materials Science and Engineering 2019; 645(1): 012022.
  • [21] Raposo F, Borja R, Ibelli-Bianco C. Predictive regression models for biochemical methane potential tests of biomass samples: Pitfalls and challenges of laboratory measurements. Renewable and Sustainable Energy Reviews 2020; 127: 109890.
  • [22] Liu K, Hu X, Wei Z, Li Y, Jiang Y. Modified Gaussian process regression models for cyclic capacity prediction of lithium-ion batteries. IEEE Transactions on Transportation Electrification 2019; 5(4): 1225-1236.
  • [23] Chen X, Huang J, Yi M. Cost estimation for general aviation aircrafts using regression models and variable importance in projection analysis. Journal of Cleaner Production, 2020; 256: 120648.
  • [24] D'Agostino R. Goodness-of-fit-techniques. Routledge. 2017.
  • [25] Hagquist C, Stenbeck M. Goodness of fit in regression analysis–R 2 and G 2 reconsidered. Quality and Quantity 1998; 32(3): 229-245.

Year 2022, Volume: 7 Issue: 2, 179 - 194, 29.12.2022

Vehbi Emrah Atasoy

https://doi.org/10.58559/ijes.1207971

Abstract

References

  • [1] Stolzer AJ. Fuel consumption modeling of a transport category aircraft using flight operations quality assurance data: a literature review. Journal of Air Transportation 2002; 7(1): 93.
  • [2] Lee DS, Pitari G, Grewe V, Gierens K, Penner JE, Petzold A, Prather MJ, Schumann U, Bais A, Berntsen T, Iachetti D, Lim LL, Sausen R. Transport impacts on atmosphere and climate: aviation, Atmospheric Environment 2010; 44(37): 4678-4734.
  • [3] Larsson J, Elofsson A, Sterner T, Åkerman J. International and national climate policies for aviation: a review. Climate Policy 2019; 19(6): 787-799.
  • [4] Brzozowski K. Kotlarz W. Modelling of air pollution on a military airfield. Atmospheric Environment 2005; 39(33): 6130-6139.
  • [5] Senzig DA, Fleming GG, Iovinelli RJ. Modeling of terminal-area airplane fuel consumption. Journal of Aircraft 2009; 46(4): 1089-1093.
  • [6] Collins B. Estimation of aircraft fuel consumption, Journal of Aircraft 1982; 19(11): 969–975.
  • [7] Patterson J, Noel GJ, Senzig DA, Roof CJ, Fleming GG. Analysis of departure and arrival profiles using real-time aircraft data. Journal of Aircraft 2009; 46(4): 1094-1103.
  • [8] Kim HJ, Baik H. Empirical method for estimating aircraft fuel consumption in ground operations. Transportation Research Record 2020; 2674(12): 385-394.
  • [9] Turgut ET, Rosen MA. Relationship between fuel consumption and altitude for commercial aircraft during descent: preliminary assessment with a genetic algorithm. Aerospace Science and Technology 2012; 17(1): 65-73.
  • [10] Zhu Q, Pei J, Liu X, Zhou Z. Analyzing commercial aircraft fuel consumption during descent: A case study using an improved K-means clustering algorithm. Journal of Cleaner Production 2019; 223: 869-882.
  • [11] Oruc R., Baklacioglu T. Modelling of fuel flow-rate of commercial aircraft for the climbing flight using cuckoo search algorithm. Aircraft Engineering and Aerospace Technology 2020; 92(3): 495-501
  • [12] Seymour K, Held M, Georges G, Boulouchos K. Fuel Estimation in Air Transportation: Modeling global fuel consumption for commercial aviation. Transportation Research Part D: Transport and Environment 2020; 88: 102528.
  • [13] Luo W, Wu Z, Chen C. An Aircraft Fuel Flow Model of Cruise Phase Based on LSTM and QAR Data. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 118-121). IEEE. 2020.
  • [14] Baumann S, Klingauf U. Modeling of aircraft fuel consumption using machine learning algorithms. CEAS Aeronautical Journal 2020; 11(1): 277-287.
  • [15] Baklacioglu T. Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks. Aerospace Science and Technology 2016; 49:52-62
  • [16] Elbir T. Estimation of engine emissions from commercial aircraft at a midsized Turkish airport. Journal of Environmental Engineering 2008; 134(3): 210-215.
  • [17] Rice CC. Validation of approach and climb-out times-in-mode for aircraft emissions computation. Transportation research record 2003; 1850(1): 79-82.
  • [18] Chati YS, Balakrishnan H. Data-driven modeling of aircraft engine fuel burn in climb out and approach. Transportation Research Record 2018; 2672(29): 1-11.
  • [19] Liati A, Schreiber D, Alpert PA, Liao Y, Brem BT, Arroyo PC, Eggenschwiler, PD. Aircraft soot from conventional fuels and biofuels during ground idle and climb-out conditions: Electron microscopy and X-ray micro- spectroscopy. Environmental Pollution 2019; 247: 658-667.
  • [20] Chilongola FD, Ahyudanari E. Aviation and aircraft engine emissions at Juanda International Airport. Materials Science and Engineering 2019; 645(1): 012022.
  • [21] Raposo F, Borja R, Ibelli-Bianco C. Predictive regression models for biochemical methane potential tests of biomass samples: Pitfalls and challenges of laboratory measurements. Renewable and Sustainable Energy Reviews 2020; 127: 109890.
  • [22] Liu K, Hu X, Wei Z, Li Y, Jiang Y. Modified Gaussian process regression models for cyclic capacity prediction of lithium-ion batteries. IEEE Transactions on Transportation Electrification 2019; 5(4): 1225-1236.
  • [23] Chen X, Huang J, Yi M. Cost estimation for general aviation aircrafts using regression models and variable importance in projection analysis. Journal of Cleaner Production, 2020; 256: 120648.
  • [24] D'Agostino R. Goodness-of-fit-techniques. Routledge. 2017.
  • [25] Hagquist C, Stenbeck M. Goodness of fit in regression analysis–R 2 and G 2 reconsidered. Quality and Quantity 1998; 32(3): 229-245.
There are 25 citations in total.

Details

Primary Language English
Subjects Aerospace Engineering
Journal Section Research Article
Authors

Vehbi Emrah Atasoy 0000-0002-6781-9278

Publication Date December 29, 2022
Submission Date November 21, 2022
Acceptance Date November 30, 2022
Published in Issue Year 2022 Volume: 7 Issue: 2

Cite

APA Atasoy, V. E. (2022). Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model. International Journal of Energy Studies, 7(2), 179-194. https://doi.org/10.58559/ijes.1207971
AMA Atasoy VE. Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model. Int J Energy Studies. December 2022;7(2):179-194. doi:10.58559/ijes.1207971
Chicago Atasoy, Vehbi Emrah. “Fuel Estimation of Commercial Aircraft for the Climb-Out Phase Using Gaussian Process Regression Model”. International Journal of Energy Studies 7, no. 2 (December 2022): 179-94. https://doi.org/10.58559/ijes.1207971.
EndNote Atasoy VE (December 1, 2022) Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model. International Journal of Energy Studies 7 2 179–194.
IEEE V. E. Atasoy, “Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model”, Int J Energy Studies, vol. 7, no. 2, pp. 179–194, 2022, doi: 10.58559/ijes.1207971.
ISNAD Atasoy, Vehbi Emrah. “Fuel Estimation of Commercial Aircraft for the Climb-Out Phase Using Gaussian Process Regression Model”. International Journal of Energy Studies 7/2 (December 2022), 179-194. https://doi.org/10.58559/ijes.1207971.
JAMA Atasoy VE. Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model. Int J Energy Studies. 2022;7:179–194.
MLA Atasoy, Vehbi Emrah. “Fuel Estimation of Commercial Aircraft for the Climb-Out Phase Using Gaussian Process Regression Model”. International Journal of Energy Studies, vol. 7, no. 2, 2022, pp. 179-94, doi:10.58559/ijes.1207971.
Vancouver Atasoy VE. Fuel estimation of commercial aircraft for the climb-out phase using gaussian process regression model. Int J Energy Studies. 2022;7(2):179-94.
 Download Cover Image